It is impossible to conduct an effective quality assurance procedure without useful test data. In most cases, staying away from production data is the best course of action. If you don’t take a methodical approach to creating test data, you can overlook critical use cases.

Therefore, we will discuss the best practices for creating, organizing, and using test data in this piece. Here, we’ll go through the many methods available for creating reliable test data. In this course, you’ll learn how to organize the mountain of test data produced with each iteration of a regression test. Data management aids will next be briefly discussed.

Let’s get started by thinking about how we can make test data.

Manual test data creation

The term “manual test data creation” refers to the practice of creating test data manually, either alone or with the aid of members of your quality assurance team or the programmers that built your product. For the manual approach, you’ll need to generate sample data and compile a set of test cases.

This method of producing test data is the simplest and most direct available. Manual test data is often prepared at the outset of a project’s execution, and it is made in a way that encompasses all permutations of inputs and outputs. This is the slowest approach, but it’s necessary for covering edge circumstances and input/output combinations that are unique to the project and can’t be created automatically.

Automated Test Data Generation

Software technologies that automate the whole procedure are used for automated test data creation. The key advantages of this approach are the high quality and speed with which produced data may be used. When opposed to utilizing manual test data, which is prone to human error, this method significantly shortens the time required to build, manage, and run tests.

Many intelligence capabilities are pre-built into automated test data creation technologies.

The essential properties of computer-generated test data are:
  • There is support for integrating realistic / real-world test data systems.
  • Disguising Personal Information

Third party tools

We may utilize one of many accessible third-party programmes to produce test data. These resources may be used to speed up the process of creating test data for a new programme or for a particular category of test cases. Data (which is equivalent to real-time data) may be pumped in massive amounts by most third-party applications, creating excellent testing circumstances. Accurate and domain-specific test data may be generated by external tools, which can also be used to supply the system with real-time data. One drawback is that, in comparison to the other methods we’ve examined, it tends to be more expensive.

Test Case Management System (TCMS) or Test Data Management Software (TDMS)

The generation of test samples is just the beginning. The true difficulty comes from having to manage the massive amounts of test data generated by repeatedly executing test cases through regression and release cycles.

It’s not simple, yet good software testing comes from efficient test data handling. 45% of testing teams said they found it difficult to manage test data and test environments in a recent research.

The fact that there are several causes of test case failure in a regression run is the primary factor. Bugs, erroneous testbed data, altered product behavior, insufficient infrastructure, etc. are just a few examples of the many possible causes. There must be a method in place for identifying causes of failure and fixing them.

Therefore, managing test data is a crucial part of the testing procedure. As a result of its analysis, we are able to pinpoint performance problems in the application and better manage test data.

Test data is notoriously difficult to manage due to its sheer bulk. The quantity of handiwork required makes the process lengthy. A Test Case Management System (TCMS) or Test Data Management System (TDMS) is useful for this purpose.

Test Data Management Software Key Features

Most of the test data management systems available on the market have certain qualities in common. The following are some of the most important qualities to look for in a test data management programme:

Data masking:
Data masking eliminates sensitive information and Personally Identifiable Information (PII) such as cellphone number or credit card details, residence number, and payment info from production data. Teams doing tests have access to unmasked test data. This guarantees that you meet the requirements of any data privacy laws, such as the General Data Protection Regulation (GDPR).

Targeted testing:
Testing the full programme each time the product is upgraded is resource-heavy. It’s a lot more work and effort, and it uses a lot of resources. Using targeted testing, we can verify just the updated code. More time may be spent testing and less resources are used.

Vast enough to provide useful findings but not so large as to overwhelm available resources; this is the goal of the finest test data management software.

Some of the best test data management tools in 2022 include:
  1. Microfocus Data Express
  2. IBM Test Data Management
  3. Appsurify
  4. PractiTest
  5. Quality


Test data management tools are required when dealing with large and complex QA projects. Testers can save significant time and effort with the help of a modern test data management tool.